Today, data is considered to be the “new oil” that fuels the information age. Advertisers and marketers, for instance, pay top dollar for data that could help them drive sales. Because of this, many organizations are attempting to capitalize on the big data explosion by collecting and storing data that their organizations and customers generate.

However, being a tradable commodity is just one of the ways big data brings value to organizations. Its real value is its insights. Data, when processed and analyzed, can provide information that may not only be used to guide decisions and actions but may also lead to new discoveries. A number of organizations have already succeeded in forming high-impact strategies because of their data efforts.

For example, a large part of Amazon’s success is due to its ability to maximize the business it generates from its customers. Its recommendation engine, which takes into consideration buyer behavior data, accurately suggests related products that customers end up purchasing. UPS is able to deliver more packages and saves millions of driving miles and gallons of fuel by using big data to optimize its fleet’s operations. Even the Internal Revenue Service is using data and analytics to fight tax fraud, bringing back billions to the government’s coffers.

Unfortunately, the pace at which data is generated can be overwhelming for many organizations. Enterprises are unable to process and analyze much of their data. Research group Forrester estimates that between 60 to 73 percent of enterprise data is unused leaving potentially groundbreaking insights undiscovered. Some are already exploring using artificial intelligence to deal with this issue. However, not all organizations are prepared to make such investments. In addition, human intuition may also be beneficial for some types of analyses.

This then creates an interesting option. If capable human resource may be limited within organizations, then crowdsourcing can be a viable alternative. Crowdsourcing overcomes human resource limitations as capable talent from all over the globe can work remotely on these efforts. The same principle behind Wikipedia and Kickstarter could help bring more people to study and analyze data that would otherwise be left untouched.

Some efforts are already showing good results taking this route. MIT researchers developed a collaboration tool called FeatureHub. The tool allows data scientist and experts from all over to collaborate on feature identification – a crucial initial step in data analysis where data points that can be considered useful metrics for particular analyses are chosen. Their attempts produced predictive models that scored within three to five points of winning entries in competitions held by online data science portal Kaggle.

For these efforts to truly work there must also be some incentive mechanism in place. Competitions and research bounties are commonplace in research communities so enterprises can offer something similar. Many tech companies already hand out monetary rewards to people who can help solve problems. Google, for example, has a bug bounty program that rewards people who can expose vulnerabilities in its systems.

Emerging technologies such as blockchain may also help in creating a transparent means to contribute to such collaborative efforts and be rewarded accordingly. For instance, cybersecurity service Hacken offers a bug bounty marketplace that uses blockchain and crypto tokens for security experts and companies to fairly trade services and rewards. Wider accessibility to cybersecurity expertise should benefit small-to-medium enterprises that are most vulnerable to security threats.

If crowdsourcing can be done for critical measures like cybersecurity, it could be done for analytics as well. Sports-oriented blockchain service BlitzPredict aims to incentivize analytics experts to share predictive models for evaluating match outcomes. The platform then monitors outcomes using the blockchain as the record so that the best-performing ones can be rewarded with crypto tokens. The blockchain prevents plagiarism of models and forgery of results. The company is also looking into sponsoring analytics tournaments that would reward the most accurate predictions. Access to such models could benefit not only fans and enthusiasts but also quite possibly smaller market leagues and teams who may not have the necessary resources to have their own in-house pool of analytics experts.

Gamification, or using game design principles to increase user engagement, can also be used to encourage people to participate in crowdsourced efforts. The Foldit project, developed by researchers from the University of Washington, uses solutions from players to help the research team unravel the mysteries of protein folding. The effort has already led to several key breakthroughs.

Aside from encouraging analysts to participate through incentives, companies must ultimately have to consider opening up their data for analysis. Organizations, especially commercial ones, can be quite protective of their data. This attitude is what particularly compels them to silo their data and leave it unused. So, all of this hinges upon organizations adopting a collaborative mindset. Their willingness to open up their data and provide compensation to those willing to help should provide enterprises with the resources to create more value from their data.

Jim Hoffer is founder and managing director at Hoffer Financial Consulting. Follow him on Twitter.